Trust in Science in the 2018 Global Health Monitor Report

library(tidyverse)
library(gt)
use("glue", "glue")
use("readxl", "read_xlsx")
use("htmltools", "HTML")
use("here", "here")
use("htmlwidgets", "onRender")
use("RColorBrewer", "brewer.pal")
use("scales", c("label_percent", "percent", "alpha"))

df <- read_xlsx(
  here(
    "wgm2018-dataset-crosstabs-all-countries.xlsx"
  ),
  sheet = 2
)

df <- "0=Not assigned, 1=Africa,2=Africa,3=Africa,4=Africa,5=Africa,6=Americas,7=Americas,8=Americas,9=Asia,10=Asia,11=Asia,12=Asia,13=Middle East,14=Former Soviet Union,15=Europe,16=Europe,17=Europe,18=Asia" |>
  str_split(",") |>
  unlist() |>
  str_trim() |>
  as_tibble() |>
  separate_wider_delim(value, "=", names = c("Regions_Report", "Region")) |>
  mutate(Regions_Report = as.numeric(Regions_Report)) |>
  right_join(df) |>
  select(-Regions_Report)

df <- "1=United States, 2=Egypt, 3=Morocco, 4=Lebanon, 5=Saudi Arabia, 6=Jordan, 8=Turkey, 9=Pakistan, 10=Indonesia, 11=Bangladesh, 12=United Kingdom, 13=France, 14=Germany, 15=Netherlands, 16=Belgium, 17=Spain, 18=Italy, 19=Poland, 20=Hungary, 21=Czech Republic, 22=Romania, 23=Sweden, 24=Greece, 25=Denmark, 26=Iran, 28=Singapore, 29=Japan, 30=China, 31=India, 32=Venezuela, 33=Brazil, 34=Mexico, 35=Nigeria, 36=Kenya, 37=Tanzania, 38=Israel, 39=Palestinian Territories, 40=Ghana, 41=Uganda, 42=Benin, 43=Madagascar, 44=Malawi, 45=South Africa, 46=Canada, 47=Australia, 48=Philippines, 49=Sri Lanka, 50=Vietnam, 51=Thailand, 52=Cambodia, 53=Laos, 54=Myanmar, 55=New Zealand, 57=Botswana, 60=Ethiopia, 61=Mali, 62=Mauritania, 63=Mozambique, 64=Niger, 65=Rwanda, 66=Senegal, 67=Zambia, 68=South Korea, 69=Taiwan, 70=Afghanistan, 71=Belarus, 72=Georgia, 73=Kazakhstan, 74=Kyrgyzstan, 75=Moldova, 76=Russia, 77=Ukraine, 78=Burkina Faso, 79=Cameroon, 80=Sierra Leone, 81=Zimbabwe, 82=Costa Rica, 83=Albania, 84=Algeria, 87=Argentina, 88=Armenia, 89=Austria, 90=Azerbaijan, 96=Bolivia, 97=Bosnia and Herzegovina, 99=Bulgaria, 100=Burundi, 103=Chad, 104=Chile, 105=Colombia, 106=Comoros, 108=Republic of Congo, 109=Croatia, 111=Cyprus, 114=Dominican Republic, 115=Ecuador, 116=El Salvador, 119=Estonia, 121=Finland, 122=Gabon, 124=Guatemala, 125=Guinea, 128=Haiti, 129=Honduras, 130=Iceland, 131=Iraq, 132=Ireland, 134=Ivory Coast, 137=Kuwait, 138=Latvia, 140=Liberia, 141=Libya, 143=Lithuania, 144=Luxembourg, 145=Macedonia, 146=Malaysia, 148=Malta, 150=Mauritius, 153=Mongolia, 154=Montenegro, 155=Namibia, 157=Nepal, 158=Nicaragua, 160=Norway, 163=Panama, 164=Paraguay, 165=Peru, 166=Portugal, 173=Serbia, 175=Slovakia, 176=Slovenia, 183=Eswatini, 184=Switzerland, 185=Tajikistan, 186=The Gambia, 187=Togo, 190=Tunisia, 191=Turkmenistan, 193=United Arab Emirates, 194=Uruguay, 195=Uzbekistan, 197=Yemen, 198=Kosovo, 202=Northern Cyprus" |>
  str_split(",") |>
  unlist() |>
  str_trim() |>
  as_tibble() |>
  separate_wider_delim(value, "=", names = c("WP5", "Country")) |>
  mutate(WP5 = as.numeric(WP5)) |>
  right_join(df) |>
  select(-WP5)

region_colours <- brewer.pal(7, "Dark2")

library(leaflet)
library(rnaturalearth)
library(sf)

Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.4.0; sf_use_s2() is TRUE

science <- df |>
  drop_na(Q1) |>
  mutate(science = Q1 < 3, across(c(Country, Region), fct)) |>
  group_by(Country, Region) |>
  summarize(percent = mean(science), .groups = "drop")

country_data <- ne_countries(scale = "medium", returnclass = "sf") |>
  mutate(
    admin = case_when(
      admin == "United States of America" ~ "United States",
      admin == "Czechia" ~ "Czech Republic",
      admin == "Palestine" ~ "Palestinian Territories",
      admin == "Republic of the Congo" ~ "Republic of Congo",
      admin == "Gambia" ~ "The Gambia",
      admin == "United Republic of Tanzania" ~ "Tanzania",
      admin == "North Macedonia" ~ "Macedonia",
      admin == "Republic of Serbia" ~ "Serbia",
      admin == "Republic of Serbia" ~ "Serbia",
      TRUE ~ admin
    )
  ) |>
  right_join(science, by = join_by(admin == Country)) |>
  drop_na(scalerank)

vaccine <- df |>
  drop_na(Q25, Q26) |>
  mutate(
    safe = Q25 %in% c(4, 5),
    effective = Q26 %in% c(4, 5),
    across(c(Country, Region), fct)
  ) |>
  group_by(Country, Region) |>
  summarize(
    safe = mean(safe),
    effective = mean(effective),
    .groups = "drop"
  )

Science

Column - 1

Perceived Science Knowledge per Country

Perceived Science Knowledge per Country

Percentage of people who answered “a lot” or “some” to the question: How much do you, personally, know about science? Do you know a lot, some, not much, or nothing at all?

country_colours <- colorNumeric(
  palette = "Purples",
  domain = country_data$percent,
  na.color = "#bdbdbd"
)

country_labels <- glue(
  "<strong>{country_data$admin}</strong><br/>{percent(country_data$percent, accuracy = 0.1)}"
) |>
  lapply(HTML)

country_data |>
  leaflet() |>
  addTiles(options = tileOptions(noWrap = TRUE)) |>
  addPolygons(
    fillColor = ~ country_colours(percent),
    weight = 1,
    opacity = 1,
    color = "white",
    dashArray = "3",
    fillOpacity = 0.7,
    label = country_labels,
    highlightOptions = highlightOptions(
      weight = 3,
      color = "#666",
      dashArray = "",
      fillOpacity = 0.9,
      bringToFront = TRUE
    ),
    group = "Countries"
  ) |> 
  onRender("
    function(el, x) {
      // Select the map container element (el) and set its background style
      el.style.backgroundColor = 'transparent';
    }
  ") 

Data

science |>
  arrange(desc(percent)) |>
  gt() |>
  fmt_percent(columns = percent, decimals = 1) |>
  cols_label(
    Country = "Country",
    percent = "Science Knowledge"
  ) |>
  opt_row_striping(FALSE) |>
  data_color(
    columns = Region,
    method = "factor",
    palette = alpha(region_colours, 0.5)
  )

Country	Region	Science Knowledge
United States	Americas	83.4%
Norway	Europe	81.7%
Turkmenistan	Asia	79.7%
Denmark	Europe	76.8%
France	Europe	75.2%
Luxembourg	Europe	74.8%
Northern Cyprus	Not assigned	73.7%
Canada	Americas	73.2%
Sweden	Europe	73.0%
Armenia	Asia	72.6%
Spain	Europe	72.3%
Belarus	Former Soviet Union	72.0%
Germany	Europe	72.0%
Switzerland	Europe	71.2%
Slovenia	Europe	70.6%
Belgium	Europe	69.7%
Ukraine	Former Soviet Union	69.3%
United Kingdom	Europe	68.1%
Finland	Europe	67.3%
Poland	Former Soviet Union	66.6%
Azerbaijan	Asia	66.4%
Portugal	Europe	65.5%
Iran	Middle East	65.3%
Australia	Asia	64.6%
Kenya	Africa	64.5%
Montenegro	Europe	64.4%
Hungary	Former Soviet Union	63.2%
Estonia	Europe	62.9%
New Zealand	Asia	62.6%
United Arab Emirates	Middle East	62.5%
Moldova	Former Soviet Union	62.3%
Sri Lanka	Asia	62.2%
Turkey	Middle East	61.5%
Singapore	Asia	61.4%
Netherlands	Europe	61.3%
Lebanon	Middle East	61.1%
Iceland	Europe	61.0%
Malta	Europe	61.0%
Cyprus	Europe	60.8%
Croatia	Europe	60.3%
Ireland	Europe	59.9%
Cameroon	Africa	59.3%
Mauritius	Africa	58.6%
Saudi Arabia	Middle East	58.5%
Russia	Former Soviet Union	58.4%
Palestinian Territories	Middle East	58.0%
Uganda	Africa	57.6%
Austria	Europe	57.6%
Jordan	Middle East	57.6%
Ivory Coast	Africa	57.4%
Chad	Africa	57.0%
Serbia	Europe	57.0%
Georgia	Asia	56.9%
Tajikistan	Asia	56.1%
Philippines	Asia	55.6%
Iraq	Middle East	55.3%
Benin	Africa	55.1%
Kuwait	Middle East	54.6%
Lithuania	Europe	54.4%
Bosnia and Herzegovina	Europe	54.3%
Algeria	Africa	54.2%
Kazakhstan	Asia	53.4%
Gabon	Africa	53.1%
Ghana	Africa	52.7%
Nepal	Asia	52.2%
Kyrgyzstan	Asia	51.8%
Peru	Americas	51.0%
Romania	Former Soviet Union	50.9%
Macedonia	Europe	50.9%
Dominican Republic	Americas	50.8%
Uzbekistan	Asia	50.7%
Libya	Africa	50.6%
Republic of Congo	Africa	50.5%
Malaysia	Asia	50.5%
Haiti	Americas	50.2%
Zambia	Africa	49.7%
Slovakia	Former Soviet Union	49.7%
Bolivia	Americas	49.1%
Namibia	Africa	48.7%
Tunisia	Africa	48.7%
Bangladesh	Asia	47.6%
South Korea	Asia	47.5%
Guinea	Africa	47.3%
Tanzania	Africa	47.2%
Burkina Faso	Africa	47.2%
Italy	Europe	47.1%
Kosovo	Not assigned	47.1%
India	Asia	47.0%
Zimbabwe	Africa	46.9%
Czech Republic	Former Soviet Union	46.7%
Taiwan	Asia	45.6%
Ethiopia	Africa	45.1%
Israel	Middle East	45.0%
Panama	Americas	44.8%
Latvia	Europe	44.7%
Nicaragua	Americas	44.3%
Togo	Africa	44.3%
Morocco	Africa	43.7%
Bulgaria	Former Soviet Union	43.7%
Argentina	Americas	43.4%
Mali	Africa	42.8%
Uruguay	Americas	42.8%
Afghanistan	Asia	42.3%
Costa Rica	Americas	42.1%
Venezuela	Americas	41.7%
Ecuador	Americas	41.7%
Liberia	Africa	41.6%
Mexico	Americas	41.3%
Nigeria	Africa	41.1%
Chile	Americas	41.0%
Mauritania	Africa	40.7%
Egypt	Africa	40.3%
Botswana	Africa	39.5%
Comoros	Africa	39.4%
Senegal	Africa	39.3%
Mongolia	Asia	39.3%
Greece	Europe	39.0%
Burundi	Africa	38.4%
Colombia	Americas	38.3%
Mozambique	Africa	38.2%
Pakistan	Asia	37.8%
Albania	Europe	37.2%
Indonesia	Asia	36.9%
Paraguay	Americas	36.4%
Thailand	Asia	36.2%
Japan	Asia	35.8%
El Salvador	Americas	35.5%
Honduras	Americas	34.9%
Yemen	Middle East	34.8%
Guatemala	Americas	33.9%
Madagascar	Africa	33.5%
South Africa	Africa	33.1%
Eswatini	Africa	31.5%
Laos	Asia	30.2%
Myanmar	Asia	29.8%
Cambodia	Asia	28.2%
Brazil	Americas	28.0%
Niger	Africa	27.2%
The Gambia	Africa	23.8%
Sierra Leone	Africa	23.1%
Vietnam	Asia	22.7%
Malawi	Africa	22.5%
China	Asia	21.6%
Rwanda	Africa	12.0%

Column - 2

Americans had the highest percentage of respondents who perceived themselves as being knowledgeable about science (~83%); however, around 10% of respondents disagreed on the safety of vaccines and about 5% of respondents disputed vaccines’ effectiveness.

Surveyed respondents from Egypt expressed some of the least disbelief in the effectiveness (~0.5%) and safety (~0.3%) amongst all respondents. This stands in stark contrast to the United States.

Vaccine Belief

Column - 1

Vaccine Safety and Efficacy

library(plotly)

Attaching package: 'plotly'

The following object is masked from 'package:ggplot2':

    last_plot

The following object is masked from 'package:stats':

    filter

The following object is masked from 'package:graphics':

    layout

p <- vaccine |>
  ggplot(aes(
    x = safe,
    y = effective
  )) +
  geom_point(aes(
    text = glue(
      "Country: {Country}<br>",
      "Region: {Region}<br>",
      "Safe: {percent(safe, accuracy = 0.01)}<br>",
      "Effective: {percent(effective, accuracy = 0.01)}"
    ),
    color = Region
  )) +
  geom_smooth(method = "lm", se = FALSE, linewidth = 0.5) +
  labs(
    x = "Disagree on Safety",
    y = "Disagree on Efficacy",
    title = "Positive Relation Between Disbelief in Vaccine Safety and Efficacy"
  ) +
  scale_x_continuous(labels = label_percent()) +
  scale_y_continuous(labels = label_percent()) +
  scale_color_manual(values = region_colours) +
  theme_bw() +
  theme(legend.position = "none")

Warning in geom_point(aes(text = glue("Country: {Country}<br>", "Region:
{Region}<br>", : Ignoring unknown aesthetics: text

ggplotly(p, tooltip = "text")

`geom_smooth()` using formula = 'y ~ x'

vaccine |>
  mutate(tmp = (safe + effective) / 2) |> 
  arrange(tmp) |>
  select(-tmp) |> 
  gt() |>
  fmt_percent(columns = c(safe, effective), decimals = 1) |>
  cols_label(
    safe = "Safety",
    effective = "Efficacy"
  ) |>
  data_color(
    columns = Region,
    method = "factor",
    palette = alpha(region_colours, 0.5)
  )

Country	Region	Safety	Efficacy
Egypt	Africa	0.3%	0.5%
Bangladesh	Asia	0.8%	0.4%
Tajikistan	Asia	1.3%	1.0%
Rwanda	Africa	1.8%	0.8%
Laos	Asia	2.1%	0.9%
India	Asia	2.1%	1.9%
Hungary	Former Soviet Union	2.5%	1.5%
Lebanon	Middle East	3.2%	1.2%
Ethiopia	Africa	3.1%	1.5%
Malawi	Africa	2.8%	1.8%
Palestinian Territories	Middle East	2.7%	1.9%
Uzbekistan	Asia	3.5%	1.3%
Singapore	Asia	2.8%	2.2%
Thailand	Asia	2.8%	2.3%
Tanzania	Africa	1.8%	3.5%
Northern Cyprus	Not assigned	4.1%	1.6%
Jordan	Middle East	3.2%	2.5%
Myanmar	Asia	3.7%	2.3%
Zimbabwe	Africa	4.1%	1.9%
Venezuela	Americas	3.8%	2.2%
Dominican Republic	Americas	4.0%	2.3%
Israel	Middle East	4.7%	1.7%
Afghanistan	Asia	4.5%	1.9%
Nicaragua	Americas	2.9%	3.6%
Greece	Europe	4.1%	2.5%
Kuwait	Middle East	4.0%	2.6%
Cambodia	Asia	5.3%	1.8%
Yemen	Middle East	3.5%	3.6%
Morocco	Africa	3.3%	4.1%
Malaysia	Asia	2.9%	4.6%
Slovakia	Former Soviet Union	5.5%	2.3%
Tunisia	Africa	4.7%	3.1%
Argentina	Americas	4.7%	3.3%
Saudi Arabia	Middle East	4.3%	3.8%
Iraq	Middle East	4.5%	3.6%
United Arab Emirates	Middle East	4.5%	3.7%
Ghana	Africa	3.9%	4.6%
Sierra Leone	Africa	4.2%	4.6%
Czech Republic	Former Soviet Union	6.1%	2.7%
Burundi	Africa	5.9%	3.1%
Norway	Europe	6.2%	2.8%
Turkmenistan	Asia	4.2%	4.8%
South Korea	Asia	6.9%	2.2%
Australia	Asia	6.0%	3.3%
Pakistan	Asia	4.7%	4.9%
Costa Rica	Americas	5.1%	4.6%
Turkey	Middle East	6.5%	3.4%
Sri Lanka	Asia	7.1%	2.9%
Spain	Europe	5.5%	4.7%
The Gambia	Africa	5.1%	5.1%
Poland	Former Soviet Union	6.0%	4.3%
Iran	Middle East	8.3%	2.0%
Panama	Americas	5.0%	5.5%
United Kingdom	Europe	8.1%	2.6%
China	Asia	7.9%	2.9%
Honduras	Americas	5.8%	5.1%
Nepal	Asia	4.0%	6.9%
Japan	Asia	8.2%	2.9%
Comoros	Africa	8.4%	2.7%
Kosovo	Not assigned	5.8%	5.4%
Niger	Africa	7.3%	4.0%
Ireland	Europe	8.0%	3.5%
Malta	Europe	6.8%	5.0%
Lithuania	Europe	7.9%	3.9%
Libya	Africa	7.1%	4.7%
Finland	Europe	7.7%	4.1%
Croatia	Europe	7.4%	4.5%
Kenya	Africa	4.7%	7.4%
Mexico	Americas	6.8%	5.4%
Vietnam	Asia	9.1%	3.2%
Ecuador	Americas	5.8%	6.5%
Cyprus	Europe	7.8%	4.7%
Serbia	Europe	8.1%	4.5%
Portugal	Europe	4.9%	8.4%
Botswana	Africa	9.2%	4.1%
Denmark	Europe	9.0%	4.4%
Sweden	Europe	10.9%	2.5%
Bosnia and Herzegovina	Europe	7.9%	5.9%
Georgia	Asia	9.2%	4.6%
Guatemala	Americas	7.6%	6.7%
Paraguay	Americas	7.0%	7.4%
Canada	Americas	8.4%	6.3%
Mozambique	Africa	5.3%	9.6%
Mauritania	Africa	10.3%	4.7%
United States	Americas	10.2%	5.0%
New Zealand	Asia	9.3%	6.1%
Brazil	Americas	9.3%	6.6%
Romania	Former Soviet Union	10.9%	5.2%
Madagascar	Africa	10.7%	5.7%
Uruguay	Americas	9.7%	6.8%
Kazakhstan	Asia	11.1%	5.8%
Kyrgyzstan	Asia	9.4%	7.9%
Colombia	Americas	10.0%	7.3%
Indonesia	Asia	5.8%	11.7%
Eswatini	Africa	10.4%	7.2%
Chile	Americas	11.5%	6.7%
Germany	Europe	14.4%	3.8%
Zambia	Africa	8.6%	9.7%
Algeria	Africa	10.7%	7.8%
Italy	Europe	13.6%	4.9%
Estonia	Europe	12.8%	6.3%
Azerbaijan	Asia	10.8%	8.3%
Luxembourg	Europe	13.7%	5.6%
Moldova	Former Soviet Union	11.3%	8.1%
El Salvador	Americas	11.5%	7.9%
Slovenia	Europe	11.8%	7.8%
Bolivia	Americas	12.3%	7.8%
South Africa	Africa	8.7%	11.3%
Chad	Africa	11.5%	8.7%
Macedonia	Europe	12.5%	7.7%
Montenegro	Europe	13.1%	7.2%
Mongolia	Asia	15.1%	5.4%
Mauritius	Africa	16.4%	4.9%
Mali	Africa	16.2%	5.5%
Uganda	Africa	8.7%	12.9%
Republic of Congo	Africa	18.4%	3.9%
Guinea	Africa	15.4%	7.3%
Iceland	Europe	21.3%	1.4%
Nigeria	Africa	6.1%	16.7%
Benin	Africa	15.0%	7.8%
Bulgaria	Former Soviet Union	15.4%	7.5%
Ukraine	Former Soviet Union	15.9%	7.8%
Albania	Europe	15.4%	8.4%
Senegal	Africa	14.3%	9.6%
Philippines	Asia	14.2%	9.8%
Cameroon	Africa	15.3%	9.0%
Belarus	Former Soviet Union	15.4%	10.0%
Taiwan	Asia	19.0%	6.9%
Ivory Coast	Africa	18.1%	8.0%
Namibia	Africa	11.0%	15.9%
Latvia	Europe	17.0%	10.0%
Belgium	Europe	21.5%	5.6%
Burkina Faso	Africa	21.2%	9.0%
Netherlands	Europe	18.4%	12.1%
Haiti	Americas	21.2%	9.5%
Liberia	Africa	2.3%	28.8%
Switzerland	Europe	22.5%	9.1%
Austria	Europe	21.0%	11.2%
Peru	Americas	17.6%	15.2%
Armenia	Asia	20.8%	13.1%
Togo	Africa	24.8%	10.2%
Russia	Former Soviet Union	23.4%	12.2%
Gabon	Africa	26.9%	11.3%
France	Europe	31.8%	15.9%

Column - 2

Vaccine Belief by Region and Country

Percentage of people who agreed with the statement: Vaccines are safe.

library(gghalves)
# Taken from https://github.com/teunbrand/ggplot_tricks?tab=readme-ov-file#lets-begin
my_fill <- aes(fill = after_scale(alpha(colour, 0.3)))
# A small nudge offset
offset <- 0.025

vaccine_info <- df |>
  drop_na(Q25) |>
  mutate(
    vaccine = Q25 < 3,
    across(c(Country, Region), fct)
  ) |>
  filter(Region != "Not assigned") |>
  group_by(Region, Country) |>
  summarize(percent = mean(vaccine), .groups = "drop_last")

vaccine_info |>
  ggplot(
    aes(
      x = fct_reorder(Region, percent),
      y = percent,
      colour = Region,
      !!!my_fill
    )
  ) +
  geom_half_violin(side = "l", trim = FALSE, scale = "width") +
  geom_half_boxplot(
    side = "l",
    coef = 0,
    width = 0.4,
    outliers = FALSE,
    alpha = 0.3
  ) +
  geom_half_dotplot(
    method = "histodot",
    stackdir = "up",
    dotsize = 1,
    binwidth = 0.01
  ) +
  scale_y_continuous(labels = label_percent(), limits = c(0, 1)) +
  scale_color_manual(values = region_colours) +
  scale_fill_manual(values = region_colours) +
  coord_flip() +
  labs(
    x = element_blank(),
    y = element_blank(),
    title = element_blank()
  ) +
  theme_bw() +
  theme(legend.position = "none")

Warning: Removed 278 rows containing missing values or values outside the scale range
(`geom_half_violin()`).

vaccine_info |>
  ungroup() |>
  mutate(Region = fct_reorder(Region, percent, .desc = TRUE)) |>
  group_by(Region) |>
  arrange(desc(percent), .by_group = TRUE) |>
  gt() |>
  fmt_percent(columns = percent, decimals = 1) |>
  cols_label(
    percent = "Vaccine Belief"
  ) |>
  data_color(
    columns = Region,
    method = "factor",
    palette = alpha(region_colours, 0.5)
  )

Country	Vaccine Belief
Asia
Bangladesh	97.7%
India	95.8%
Afghanistan	94.2%
Thailand	93.1%
Uzbekistan	92.3%
Myanmar	92.1%
Laos	92.1%
Tajikistan	91.1%
Nepal	90.9%
Malaysia	90.2%
Cambodia	89.8%
Turkmenistan	87.4%
Pakistan	86.7%
Sri Lanka	86.6%
Australia	85.2%
Indonesia	84.4%
Philippines	78.0%
Singapore	75.3%
Mongolia	74.8%
New Zealand	74.6%
China	74.1%
Vietnam	73.0%
Kyrgyzstan	71.2%
Georgia	68.6%
Azerbaijan	63.5%
Taiwan	53.9%
South Korea	51.8%
Kazakhstan	44.2%
Armenia	43.6%
Japan	34.4%
Middle East
Palestinian Territories	92.1%
Jordan	90.8%
Iraq	89.7%
Kuwait	86.9%
Lebanon	85.0%
Yemen	84.7%
United Arab Emirates	83.5%
Saudi Arabia	82.4%
Turkey	79.6%
Israel	75.6%
Iran	68.0%
Americas
Nicaragua	93.5%
Venezuela	92.3%
Dominican Republic	90.8%
Mexico	88.5%
Honduras	88.3%
Argentina	87.1%
Costa Rica	87.1%
Panama	86.4%
Guatemala	86.3%
Ecuador	84.7%
Colombia	81.7%
Brazil	81.1%
El Salvador	77.4%
Paraguay	77.2%
Canada	76.6%
United States	75.8%
Bolivia	75.2%
Chile	75.1%
Uruguay	74.0%
Haiti	70.1%
Peru	69.8%
Africa
Egypt	97.1%
Liberia	96.5%
Ethiopia	95.7%
Tanzania	95.6%
Rwanda	94.2%
Sierra Leone	92.9%
Malawi	92.8%
Kenya	90.8%
Mozambique	89.9%
Nigeria	89.2%
Burundi	88.5%
Comoros	87.9%
The Gambia	87.3%
Ghana	86.4%
Zimbabwe	85.9%
Niger	85.2%
Madagascar	84.4%
Uganda	84.3%
Namibia	83.9%
Zambia	81.7%
Chad	81.2%
South Africa	81.1%
Botswana	80.8%
Benin	80.6%
Morocco	78.9%
Tunisia	78.3%
Guinea	78.3%
Libya	78.2%
Ivory Coast	75.9%
Eswatini	74.9%
Republic of Congo	73.8%
Mauritania	73.7%
Senegal	73.0%
Cameroon	72.4%
Mali	69.7%
Burkina Faso	67.9%
Algeria	64.9%
Mauritius	61.0%
Gabon	60.8%
Togo	53.8%
Europe
Spain	82.5%
Norway	82.1%
Portugal	80.5%
Malta	78.3%
United Kingdom	77.4%
Greece	76.9%
Ireland	76.9%
Finland	76.7%
Italy	75.1%
Sweden	72.6%
Slovenia	71.6%
Bosnia and Herzegovina	70.9%
Denmark	70.5%
Luxembourg	69.3%
Serbia	67.8%
Netherlands	67.3%
Germany	66.7%
Cyprus	64.1%
Iceland	62.4%
Albania	61.5%
Belgium	60.9%
Croatia	58.0%
Austria	55.8%
Macedonia	55.2%
Lithuania	53.5%
Switzerland	51.9%
France	50.9%
Estonia	48.6%
Montenegro	44.6%
Latvia	44.2%
Former Soviet Union
Hungary	78.9%
Poland	76.4%
Slovakia	74.2%
Czech Republic	65.2%
Romania	64.8%
Russia	43.7%
Moldova	43.0%
Bulgaria	39.4%
Belarus	37.8%
Ukraine	30.2%